Enhancing NUE in Corn Through Optimized Sensor-Based Prescription Maps

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Abstract
Enhancing nitrogen use efficiency (NUE) through optimized application methods can benefit agronomic productivity and environmental sustainability. This study examined three nitrogen application strategies, flat rate, soil-based sensing, and remote sensing-based prescription maps, for corn in southeast South Dakota, USA. Soil-based sensing utilized an electrical conductivity (EC) sensor while the normalized difference vegetation index (NDVI) was extracted from remote sensing data using Sentinel-2 images to create different zones. In the flat-rate method, nitrogen is applied uniformly at all plots, regardless of field variations. On the other hand, the sensor-based methods recommended variable rates of nitrogen applications to address field variations. The results of the present study showed that remote sensing-based methods significantly identify field variations as different zones (p < 0.05). The remote sensing-based method improved NUE compared to the flat-rate method, with increases of 2.21, 29.24, 29.6, and 82.09% in zones 1, 2, 3, and 4, respectively. However, adjusting the spatial and temporal nitrogen requirement rates using a soil-based sensor was difficult. The findings suggest remote sensing-based method can offer nitrogen optimization by incorporating in-season environmental variability, enhancing agronomic efficiency and sustainability.

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